Automatic Organ Segmentation Tool for Radiation Treatment Planning of Cancers

用于癌症放射治疗计划的自动器官分割工具

• 批准号: 10221655
• 负责人: Xue Feng
• 金额: $ 100万
• 依托单位: CARINA MEDICAL, LLC
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10221655
• 关键词: 3-Dimensional; Address; Adopted; Adoption; Algorithms; American; Area; Artificial Intelligence; Atlases; Attention; Body Regions; Body part; Cancer Patient; Chest; Clinical; Clinical Research; Computer Vision Systems; Computer software; Consumption; Data; Development; Digital Imaging and Communications in Medicine; Dose; Early Diagnosis; Environment; Healthcare; Heterogeneity; Hour; Human; Image; Intraobserver Variability; Label; Malignant Neoplasms; Manuals; Measures; Medical; Medicine; Methods; Modality; Modeling; Online Systems; Organ; Outcome; Patient-Focused Outcomes; Performance; Phase; Process; Protocols documentation; Radiation Dose Unit; Radiation therapy; Risk; Scanning; Site; Slice; Survival Rate; Techniques; Testing; Time; Toxic effect; Treatment Cost; Update; X-Ray Computed Tomography; algorithm development; automated segmentation; base; cancer radiation therapy; cancer therapy; clinical heterogeneity; cloud based; commercialization; convolutional neural network; cost; deep learning; deep learning algorithm; dosimetry; healthcare community; imaging modality; improved; innovation; learning strategy; life-long learning; millimeter; novel; phase 1 study; prototype; satisfaction; segmentation algorithm; simulation; software development; success; tool; treatment planning; usability; user-friendly; validation studies

ABSTRACT As early detection and better treatment have increased cancer patient survival rates, the importance of protecting normal organs during radiation treatment is drawing more attention, which is critical in reducing long term toxicity of cancers. To avoid excessively high radiation doses to organs-at-risk (OARs), OARs need to be correctly segmented from simulation computed tomography (CT) scans during radiation treatment planning to get an accurate dose distribution. Despite tremendous effort in developing semi- or fully-automatic segmentation solutions, current automated segmentation software, mostly using the atlas-based methods, has not yet reached the level of accuracy and robustness required for clinical usage. Therefore, in current practice, significant manual efforts are still required in the OAR segmentation process. Manual contouring suffers from inter- and intra-observer variability, as well as institutional variability where different sites adopt distinct contouring atlases and labeling criteria, thus leading to inaccuracy and variability in OAR segmentation. When OARs are very close to the treatment target, segmentation errors as small as a few millimeters can have a statistically significant impact on dosimetry distribution and outcome. In addition, it is also costly and time consuming as it can take 1-2 hours of a clinicians’ time to segment major thoracic organs due to the large number of axial slices required. In summary, an accurate and fast process for segmenting OARs in treatment planning using CT scans is needed for improving patient outcomes and reducing the cost of radiation therapy of cancers. In recent years, the rapid development of deep learning methods has revolutionized many computer-vision areas and the adoption of deep learning in medical applications has shown great success. Based on a deep-learning-based algorithm we developed that achieved better-than-human performance and ranked 1st in 2017 American Association of Physicist in Medicine Thoracic Auto-segmentation Challenge, an automatic OAR segmentation product will be developed in this project with the three aims: 1) further improve the performance and robustness of OAR segmentation algorithms, focusing on addressing the heterogeneity issue of different clinical environments; 2) further enrich the functionalities and enhance usability of the cloud- based software product; and 3) perform clinical validation study on the algorithm performance and software usability at collaborating sites. With this product, the segmentation accuracy can be improved, leading to more robust treatment plans in protecting normal organs and improved long term patient outcome. The time and cost of radiation treatment planning can be greatly reduced, contributing to a more affordable cancer treatment and reduced healthcare burden.

抽象的 随着早期检测和更好的治疗增加了癌症患者的存活率， 在辐射治疗期间保护正常的器官正在引起更多注意，这对于减少长时间至关重要 癌症的术语毒性。为避免高辐射剂量到风险的器官（桨），桨需要为 在放射治疗计划期间正确分割了模拟计算机断层扫描（CT）扫描到 获得准确的剂量分布。尽管巨大的努力在开发半自动或完全自动 分割解决方案，当前的自动分割软件，主要是使用基于ATLAS的方法 尚未达到临床使用所需的准确性和鲁棒程度。因此，在当前的实践中， 在OAR分割过程中，仍然需要进行重大的手动工作。手动轮廓遭受 观察者间和观察者内变异性以及机构可变性，不同的站点采用不同的站点 轮廓图谱和标记标准，从而导致OAR分割的不准确性和可变性。什么时候 OAR非常接近治疗目标，分割误差很小，只有几毫米可以具有 对剂量分布和结果的统计学意义。此外，这也是昂贵的时间 消费，因为临床医生可能需要1-2个小时的时间才能分割主要的胸腔器官 需要轴向切片数。总而言之，在治疗中分割桨的准确而快速的过程 需要计划使用CT扫描来改善患者预后和降低放射治疗的成本 癌症。近年来，深度学习方法的快速发展彻底改变了许多 计算机视觉领域和在医疗应用中采用深度学习已取得了巨大的成功。 基于我们开发的基于深度学习的算法，它取得了比人类的表现更好的 在2017年美国医学物理学家胸腔自动分割挑战中排名第一，这是一个 该项目将开发自动OAR细分产品，其目的是：1）进一步改善 OAR分割算法的性能和鲁棒性，重点是解决异质性 不同的临床环境问题； 2）进一步丰富了云的功能并提高了云的可用性 基于的软件产品； 3）对算法性能和软件进行临床验证研究 协作网站的可用性。使用该产品，可以提高分割精度，从而导致更多 强大的治疗计划，以保护正常器官并改善长期患者结局。时间和成本 可以大大减少辐射治疗计划，从而导致更实惠的癌症治疗和 减轻医疗保健负担。